Decision-making Paradigm Research Articles

Prefrontal cortex stimulation normalizes deficient adaptive learning from outcome contingencies in low mood

Depression and anxiety are associated with deficits in adjusting learning behaviour to changing outcome contingencies. This is likely to drive and maintain symptoms, for instance, by perpetuating negative biases or a sense of uncontrollability. Normalising such deficits in adaptive learning might therefore be a novel treatment target for affective disorders. The aim of this experimental medicine study was to test whether prefrontal cortex transcranial direct current stimulation (tDCS) could normalise these aberrant learning processes in depressed mood. To test proof-of-concept, we combined tDCS with a decision-making paradigm that manipulates the volatility of reward and punishment associations. 85 participants with low mood received tDCS during (or before) the task. In two sessions participants received real or sham tDCS in counter-balanced order. Compared to healthy controls (n = 40), individuals with low mood showed significantly impaired adjustment of learning rates to the volatility of loss outcomes. Prefrontal tDCS applied during task performance normalised this deficit, by increasing the adjustment of loss learning rates. As predicted, prefrontal tDCS before task performance (control) had no effect. Thus, the effect was cognitive-state dependent. Our study shows, for the first time, that a candidate depression treatment, prefrontal tDCS, when paired with a task, can reverse deficits in adaptive learning from outcome contingencies in low mood. Thus, combining neurostimulation with a concurrent cognitive manipulation is a potential novel strategy to enhance the effect of tDCS in depression treatment.

Human AI Collaboration in Decision-Making Auto Systems With AI

The integration of Artificial Intelligence (AI) with human decision-making processes has led to the emergence of advanced automated systems designed to enhance efficiency, accuracy, and adaptability across various domains. This research investigates the collaborative dynamics between human decision-makers and AI-driven systems, focusing on their synergistic potential in automated decision-making frameworks. By combining human intuition and expertise with the computational power of AI, these systems enable optimized decision-making in complex environments. The study explores applications across industries such as healthcare, finance, and autonomous vehicles, highlighting their impact on productivity and innovation. Challenges, including ethical considerations, transparency, and trust, are critically analyzed to ensure responsible implementation. This research further examines how human oversight complements AI capabilities, fostering robust systems that balance automation with accountability. Through interdisciplinary analysis and empirical evidence, the study underscores the transformative potential of human-AI collaboration in reshaping decision-making paradigms. The findings contribute to the ongoing discourse on the future of human-machine synergy, offering actionable insights for policymakers, industry leaders, and researchers.

A Paradigm for Shared Decision-Making in Pediatric Anesthesia Practice for Children with Autism for the Generalist Clinician.

A Paradigm for Shared Decision-Making in Pediatric Anesthesia Practice for Children with Autism for the Generalist Clinician.

The use of biologics in food allergy management.

Patients and families living with food allergy may experience significant burdens, including social isolation, impaired quality of life, and anxiety. Allergists/immunologists play a critical role in educating families living with food allergies about risk, particularly with regard to the rarity of fatal food allergy. Appropriate risk framing can greatly decrease the fear-based burden of disease. In 2024, an increasing complex fabric of food allergy treatments has emerged that includes oral immunotherapy (OIT), sublingual immunotherapy (SLIT), and omalizumab, with the promise of additional treatments, including epicutaneous immunotherapy and oral mucosal immunotherapy in the near future. Younger children may be most likely to benefit from OIT and SLIT, with some evidence that suggests the possibility of an immunomodulatory effect. Omalizumab, approved in 2024 for use in conjunction with strict avoidance, increases the threshold of reactivity before a moderate-to-severe reaction for many, but not all, patients. There is no evidence to date that omalizumab has an immunomodulatory effect, and young children treated with omalizumab monotherapy may bear a lost opportunity cost from possible immunomodulation would they have been treated with OIT or SLIT instead; however, within a shared decision-making paradigm, beyond label use of omalizumab may include treatment with OIT or SLIT. Fortunately, the co-evolution of shared decision-making with modern food allergy treatments will facilitate the critical preference-sensitive care that must be characteristic of all decisions surrounding active food allergy management.

A multi-objective optimization decision-making methodology for fostering synergies in the water-energy-food nexus

Current decision-making methods within the water-energy-food nexus (WEFN) encounter challenges in practicality, portability, scalability, and accuracy. Optimization methods, integrating site-specific data, offer promise for achieving desired outcomes and enhancing practical decision implementation within WEFN systems. However, these methods still struggle with solving multi-objective and multi-constraint problems, poor performance, and decision-making difficulties. This study developed an optimization method for WEFN systems, which integrates an evolutionary algorithm, swarm intelligence algorithm, multistage evolutionary algorithm, and post-optimization theories to address these issues. Performance results demonstrated that the proposed algorithm outperformed traditional metaheuristic optimization algorithms. Specifically, it excelled in tackling high-dimensional constrained problems, surpassing the classical NSGA series algorithms with a 1.34-fold improvement in the comprehensive performance metric HV. Subsequently, this algorithm combined with knee point post-optimization theory was applied to a typical arid region known for food and energy production. Compared with the business-as-usual scenario, the optimized schemes enhance agricultural benefits, saving 13.3 billion m3 of water over the entire planning period. Meanwhile, sustainable energy implementation would yield potential carbon emission reduction benefits of around 2.8 billion CNY. In summary, the proposed method would successfully provide a paradigm for the synergetic decision-making of WEFN systems.

The Predictive Role of visual attention bias in aggressive driving decisions among violation-involved drivers on attitudes of right-of-way

This study, based on dual-process theory, explores the aggressive driving decision-making characteristics and cognitive processing of violation-involved drivers in right-of-way infringement scenarios. It aims to identify an eye-movement indicator that can predict drivers’ prosocial behaviors. The study recruited 75 drivers aged 19–58 years, who completed a video-based aggressive driving decision-making test and the Driver Attitudes of Right-of-way Questionnaire (DARQ). The results indicate that age moderates the relationship between violation history and visual attentional bias. Younger drivers with a history of violations exhibit an attentional bias towards aggressive words, whereas older drivers do not. After controlling for age, violation-involved drivers demonstrated a higher rate of aggressive decision-making, especially in situations with very short lane-change time headway. Visual attentional bias towards aggressive words can effectively predict positive emotions in attitudes towards the right-of-way. This suggests that early eye movement indicators during the driving decision-making process represent a form of socioemotional characteristic. The more positive the driver’s right-of-way attitude, the stronger their prosocial behavior and the weaker their intuitive impulsiveness during the early cognitive processing stages of driving decision-making. This indicates that the driving decision-making eye-movement assessment paradigm is an objective and effective method for evaluating drivers’ pro-sociality.

Exploring the Relationship between Top Management Team Characteristics and Corporate Social Responsibility: A Literature Review and Bibliometric Analysis

In the evolving landscape of corporate governance, the role of Top Management Teams (TMTs) has transcended traditional decision-making paradigms, becoming integral to the implementation of Corporate Social Responsibility (CSR). While the existing literature has identified general trends in TMT diversity, stability, and leadership styles, there is a lack of comprehensive analysis focusing on the interplay of these characteristics and their direct implications for CSR strategies. This study employs a literature review and bibliometric analysis of the existing literature up to 2023, utilizing the Scopus database to discern trends and patterns in the TMT–CSR relationship. Findings reveal that TMT characteristics, including diversity in gender, age, and professional background, significantly influence CSR strategies, enhancing organizations’ responsiveness to stakeholder needs. Notably, diverse TMTs demonstrate a greater capacity for developing comprehensive CSR initiatives, particularly when led by executives committed to sustainability and ethical practices. The analysis indicates a growing scholarly interest in this intersection, with a marked increase in publications over the past decade, highlighting the strategic importance of TMTs in shaping CSR outcomes. However, the identified research gaps suggest a need for further exploration of context-specific approaches, particularly in varying regional and industry settings, as well as longitudinal studies to capture the dynamic nature of TMT–CSR relationships over time.

Generalized Encoding of the Relative Subjective Value of Cognitive Effort in the Dorsal ACC.

Making choices about whether and when to engage cognitive effort are a common feature of everyday experience, with important consequences for academic, career, and health outcomes. Yet, despite their hypothesized importance, very little is understood about the underlying mechanisms that support this form of human cost-benefit decision-making. To investigate these mechanisms, we used the Cognitive Effort Discounting Paradigm (Cog-ED) during fMRI scanning to precisely quantify the neural encoding of varying cognitive effort demands relative to reward outcomes, within two distinct cognitive domains (working memory, speech comprehension). The findings provide strong evidence that the dorsal anterior cingulate cortex (dACC) plays a central and selective role in this decision-making process. Trial-by-trial modulations in dACC activation tracked the relative subjective value of the low-effort, low-reward option, with the strongest activity occurring when this was of greater value than the high-effort, high-reward option. In contrast, dACC activity was not modulated by decision difficulty, though such effects were found in other frontoparietal regions. Critically, dACC activity was also strongly correlated across the two decision-making task domains and further predicted subsequent choice behavior in both. Together, the results suggest that dACC activity modulation reflects a domain-general valuation comparison mechanism, which acts to bias participants away from decisions to engage in cognitive effort, when the perceived subjective costs of such engagement outweigh the reward-related benefits. These findings complement work in other cost domains and species by pointing to a clear role of the dACC in representing subjective value differences between choice options during cost-benefit decision-making.

Chest tube size selection for pleural effusion: from the perspective of thoracic surgeons and pulmonologists

ABSTRACT Background The current discourse within the thoracic surgical and pulmonological communities pertains to a contentious debate over the optimal selection criteria for thoracostomy tube diameters utilized in the management of pleural effusions. A comprehensive examination of the variables that inform the clinical decision-making paradigm for the determination of appropriate chest tube calibers is imperative to enhance patient management and elevate the prognostic results. Objectives The objective of this inquiry is to elucidate the determinants that influence thoracic surgeons and pulmonologists in their selection of chest tube size for the management of pleural effusions. Methods This cross-sectional study was based on an electronic questionnaire that was sent to the targeted populations through e-mail or a professional WhatsApp. The survey assessed the considerations of chest tube size selection as well as the respective advantages, disadvantages, and potential complications related to each size. Results The conducted study encompassed participants, with a nearly even distribution between thoracic surgeons (49.1%) and pulmonologists (50.9%). Most of these practitioners are within tertiary-level medical institutions (82.1%). A preference for small-bore chest tubes (SBCT), defined as < 14 French (Fr), was indicated by 54.8% of participants. The drawbacks associated with SBCT, such as kinking (60%) and blockage (70%), influenced the decision-making process negatively, while pain was a significant factor in the selection against LBCT (64%). Ultrasound guidance was a positive influence for the selection of SBCT (55%). Complications associated with LBCT included visceral and vascular injuries (55.7%), wound infection (45.3%), re-expansion pulmonary edema (43.3%), and subcutaneous emphysema (57.5%). In contrast, malposition was a complication more commonly associated with SBCT (49.1%). Conclusion The decision regarding chest tube size was influenced by several critical factors which included the nature of pleural effusion, the volume of pleural fluid, and potential complications specific to the size of the chest tube used.

Toward More Nature-Positive Outcomes: A Review of Corporate Disclosure and Decision Making on Biodiversity

Loss of biodiversity and natural degradation are vital issues that have significant impacts on society and economy. Businesses, investors, and regulators have focused on corporate efforts to support biodiversity and nature-positive activities. This review provides a comprehensive overview of the importance of biodiversity for businesses, its materiality, and the roles of mandatory and nonmandatory regulations in corporate environmental reporting and sustainability disclosure frameworks. It also discusses descriptive information on the evolution of sustainability frameworks by comparing the most prominent sustainability frameworks, with a key focus on the materiality approach and biodiversity-related disclosure recommendations. Furthermore, we provide recommendations for more holistic approaches to improve future sustainability frameworks focusing on the impact of biodiversity. Additionally, we demonstrate the necessity for greater focus on the decision-making paradigm. Further research to measure the impact of biodiversity and innovative trends in sustainability reporting is required to better reflect nature-positive outcomes in corporate sector businesses.

Data-driven risk stratification and precision management of pulmonary nodules detected on chest computed tomography

The widespread implementation of low-dose computed tomography (LDCT) in lung cancer screening has led to the increasing detection of pulmonary nodules. However, precisely evaluating the malignancy risk of pulmonary nodules remains a formidable challenge. Here we propose a triage-driven Chinese Lung Nodules Reporting and Data System (C-Lung-RADS) utilizing a medical checkup cohort of 45,064 cases. The system was operated in a stepwise fashion, initially distinguishing low-, mid-, high- and extremely high-risk nodules based on their size and density. Subsequently, it progressively integrated imaging information, demographic characteristics and follow-up data to pinpoint suspicious malignant nodules and refine the risk scale. The multidimensional system achieved a state-of-the-art performance with an area under the curve (AUC) of 0.918 (95% confidence interval (CI) 0.918–0.919) on the internal testing dataset, outperforming the single-dimensional approach (AUC of 0.881, 95% CI 0.880–0.882). Moreover, C-Lung-RADS exhibited a superior sensitivity compared with Lung-RADS v2022 (87.1% versus 63.3%) in an independent cohort, which was screened using mobile computed tomography scanners to broaden screening accessibility in resource-constrained settings. With its foundation in precise risk stratification and tailored management, this system has minimized unnecessary invasive procedures for low-risk cases and recommended prompt intervention for extremely high-risk nodules to avert diagnostic delays. This approach has the potential to enhance the decision-making paradigm and facilitate a more efficient diagnosis of lung cancer during routine checkups as well as screening scenarios.

Spatiotemporal dynamics of locomotor decisions in Drosophila melanogaster.

Decision-making in animals often involves choosing actions while navigating the environment, a process markedly different from static decision paradigms commonly studied in laboratory settings. Even in decision-making assays in which animals can freely locomote, decision outcomes are often interpreted as happening at single points in space and single moments in time, a simplification that potentially glosses over important spatiotemporal dynamics. We investigated locomotor decision-making in Drosophila melanogaster in Y-shaped mazes, measuring the extent to which their future choices could be predicted through space and time. We demonstrate that turn-decisions can be reliably predicted from flies' locomotor dynamics, with distinct predictability phases emerging as flies progress through maze regions. We show that these predictability dynamics are not merely the result of maze geometry or wall-following tendencies, but instead reflect the capacity of flies to move in ways that depend on sustained locomotor signatures, suggesting an active, working memory-like process. Additionally, we demonstrate that fly mutants known to have sensory and information-processing deficits exhibit altered spatial predictability patterns, highlighting the role of visual, mechanosensory, and dopaminergic signaling in locomotor decision-making. Finally, highlighting the broad applicability of our analyses, we generalize our findings to other species and tasks. We show that human participants in a virtual Y-maze exhibited similar decision predictability dynamics as flies. This study advances our understanding of decision-making processes, emphasizing the importance of spatial and temporal dynamics of locomotor behavior in the lead-up to discrete choice outcomes.

Optimizing ketogenic diet therapy for childhood epilepsy: Identifying key factors for seizure control and psychomotor enhancement.

To identify key factors influencing the therapeutic efficacy of the ketogenic diet (KD) for children with drug-resistant epilepsy and elucidate their interconnected relationships to optimize clinical practice. Participants were selected from children receiving KD treatment at West Second University Hospital of Sichuan University from September 2015 to October 2023. Clinical factors pre-KD and post-KD (at the third month) were analyzed systematically using an analytical framework. Descriptive analyses, univariate analyses, and multivariate regression analyses were performed for the entire cohort and subgroups of genetic and non-genetic (i.e., structural and unknown) etiologies. Thereby, the most significant predictors were identified for each relevant dependent variable. Path analysis diagrams were used for visual representation. Of 156 patients, genetic etiology was prevalent (38.5%). In the genetic subgroup, channelopathies predicted lower baseline seizure frequency and increased chance of seizure freedom with KD. Frequent seizures and complex history of anti-seizure medications (ASMs) predicted severe baseline psychomotor abnormalities. Younger age at KD initiation benefited psychomotor improvement. In the non-genetic subgroup, lower baseline seizure frequency increased the likelihood of seizure freedom post-KD. Concurrent use of multiple ASMs helped achieve ≥50% seizure reduction. Boys were more likely to experience psychomotor improvement. A significant correlation was found between ≥50% seizure reduction and psychomotor improvement in both subgroups. Delayed KD initiation (longer epilepsy duration at KD start) was related to a greater number of ASMs used, infrequent seizures, and older age at epilepsy onset. In addition, patients with channelopathies had delayed initiation of KD. Children with genetic epilepsy display more pronounced characteristics of epileptic encephalopathy. Early KD intervention is crucial for channelopathies, notably SCN1A variants. For other drug-resistant epilepsy cases, KD alongside diverse ASMs may improve seizure control and developmental outcomes. However, the patient population benefiting most from early KD tends to start the treatment later, urging a re-evaluation of KD decision-making paradigms.

A new probability forecasting model for cotton yarn futures price volatility with explainable AI and big data

AbstractCotton, cotton yarn, and other cotton products have frequent price volatility, increasing the difficulty for industry participants to develop rational business decision plans. To support cotton textile industry decision‐makers, we apply data mining methods to extract the main influencing factors affecting cotton yarn futures prices from big data and build a probabilistic forecasting model for cotton yarn price volatility with uncertainty assessment. Based on Explainable Artificial Intelligence (XAI) and data‐driven perspectives, we use the LassoNet algorithm to extract 18 features most relevant to the target variable from the massive data and visualize the importance values of the selected features to improve the reliability. Moreover, by combining conformal forecasting (CP) with quantile regression (QR), the uncertainty measure of the point estimation results of the long and short‐term memory (LSTM) model is applied to improve the application value of the model. Finally, SHAP (SHapley Additive exPlanations) is introduced to analyze the SHAP values of the input features on the output results and to explore in depth the interaction and mechanism of action between the input features and the target variables to improve the explainability of the model. Our model provides a “big data‐forecasting model‐decision support” decision paradigm for real‐world problems.

Reduced lateralization of the language network in the blind and its relationship with white matter tract neuroanatomy.

Several previous studies reported reduced leftward lateralization in blind participants' samples compared to the sighted population. The origins of this difference remain unknown. Here, we tested whether functional lateralization is connected with the structural characteristics of white matter tracts [corpus callosum (CC), uncinate fasciculus (UF), and superior longitudinal fasciculus (SLF)], as suggested by previous studies conducted in the typical sighted population. Twenty-three blind and 21 sighted adult participants were tested during fMRI with a semantic decision paradigm presented both auditorily and in the modality appropriate for reading (tactually for the blind and visually for the sighted). Lateralization indices (LI) were calculated based on the activations. The fractional anisotropy (FA) measure was extracted from the white matter tracts of interest. Correlation analyses testing the relationship between FA and LI were conducted. The reduced leftward lateralization of both speech processing and reading-related activations was replicated. Nevertheless, the relationship between the structural integrity of the CC and LI and between the asymmetry of the intrahemispheric tracts and LI was not confirmed, possibly due to the lack of power. The sources of the reduced lateralization of the language network in the sensory-deprived population remain unknown. Further studies should account for environmental variables (e.g., the frequency of contact with written language) and the complexity of the factors that may influence the functional lateralization of the human brain.

Disorders of compulsivity: Deficits in arbitrating learning strategies.

While previous research has shown that compulsivity is related to an imbalance between goal-directed and habitual learning systems, very little is known about whether this effect is due to the impairment of a single system or the impairment of the arbitration mechanism that determines which system controls behaviour at any given moment; the current study aims to address this disagreement. Nineteen alcohol use disorder, 30 obsessive-compulsive disorder (OCD) and 20 major depressive disorder patients and corresponding sex- and age-matched controls performed two-choice, three-stage Markov decision-making paradigm. Model-based and mode-free reinforcement learning models were used to independently fitted their behavioural data. Alcohol use disorder and OCD patients showed less model-based strategy choice than healthy controls in task conditions where the model-based strategy was optimal. Only OCD patients showed higher behavioural control system switching in task conditions where model-free use was optimal. Major depressive disorder patients did not differ from the matched control in both. These findings suggest that dysfunction in arbitration control between dual systems may be the basis for diverse disorders involving compulsivity.

Neural Dynamics of Processing Inflectional Morphology: An fMRI Study on Korean Inflected Verbs.

The present study aimed to elucidate the neural mechanisms underpinning the visual recognition of morphologically complex verbs in Korean, a morphologically rich, agglutinative language with inherent polymorphemic characteristics. In an fMRI experiment with a lexical decision paradigm, we investigated whether verb inflection types (base, regular, and irregular) are processed through separate mechanisms or a single system. Furthermore, we explored the semantic influence in processing inflectional morphology by manipulating the semantic ambiguity (homonymous vs. unambiguous) of inflected verbs. The results showed equivalent activation levels in the left inferior frontal gyrus for both regular and irregular verbs, challenging the dichotomy between the two. Graded effects of verb regularity were observed in the occipitotemporal regions, with regular inflections eliciting increased activation in the fusiform and lingual gyri. In the middle occipital gyrus, homonyms showed decreased activation relative to that of unambiguous words, specifically for base and irregular forms. Furthermore, the angular gyrus exhibited significant modulation with all verb types, indicating a semantic influence during morphological processing. These findings support single-system theories and the connectionist framework, challenging the assumptions of purely orthographic morphological decomposition and dual-mechanism accounts. Furthermore, they provide evidence for a semantic influence during morphological processing, with differential reliance on semantic activation for regular and irregular inflections.

Test-retest reliability of the "Home Town Walk" fMRI paradigm for memory activation and lateralization in the pre-surgical evaluation of patients with temporal lobe epilepsy.

Functional magnetic resonance imaging (fMRI) can be used to assess language and memory function as part of pre-surgical decision making in refractory epilepsy. Although language paradigms are well established, memory paradigms are not widely used in clinical practice due to a lack of evidence for robust and reliable methods. Here, we aim to investigate the clinical utility of the Home Town Walk (HTW) paradigm for personalized treatment decisions in medial temporal lobe epilepsy. A cohort of 123 consecutive patients having HTW-fMRI as part of routine MRI scans over a 7.5 year period were included in this retrospective study. Of these, 111 patients underwent repeated HTW-fMRI in two scanning sessions one to three days apart. fMRI analysis was performed at the time of the scans using clinically approved software and retrospectively validated using FSL. We assessed the test-retest within subject reliability of activations within the posterior parahippocampal gyri (pPHG) at the individual subject level. Activations within the pPHG region were observed for 101 patients (91%) in at least one of the fMRI sessions and for 88 patients (79%) in both fMRI sessions, with 82 patients showing overlapping unilateral or bilateral activations and 8 further patients showing overlapping activations in one of the hemispheres but not the other. Reproducibility was evaluated using metrics based on the concordance ratios for size (Rsize) and location (Roverlap) within the pPHG region, as well as the lateralization index (LI) metric to reflect the asymmetry of hemispheric activations, which is of crucial relevance to inform surgery. Test-retest reliability of visuospatial memory LIs, assessed by an intra-class correlation coefficient (ICC) yielded a value of 0.76, indicating excellent between session stability of memory lateralization. The HTW-fMRI paradigm shows reproducible activations in the medial temporal lobes of individual epilepsy patients sufficient to consistently lateralize visuospatial memory function, demonstrating the clinical utility of HTW memory fMRI and its potential for application in the pre-surgical assessment of people with temporal lobe epilepsy.

Optimizing Drone Logistics: A Scoring Algorithm for Enhanced Decision Making across Diverse Domains in Drone Airlines

The complexities of decision-making in drone airlines prove to be pivotal and challenging as the dynamic environment introduces variability and many decisions are conventionally static. This paper introduces an advanced decision-making system designed for the multifaceted landscape of drone applications. Our proposed system addresses various aspects, including drone assignment, safety zone sizing, priority determination, and more. The scoring model enhances adaptability in real-time scenarios, particularly highlighted by the dynamic adjustment. Based on the scenario concerning the definition of the safety zone, we have successfully applied this method and evaluated all potential scores. The user-friendly and intuitive configuration further augments the system’s accessibility, facilitating efficient deployment. In essence, the proposed system stands as an innovative approach with decision-making paradigms in the dynamic landscape of drone operations.

Moonshot. Long shot. Or sure shot. What needs to happen to realize the full potential of AI in the fertility sector?

Quality healthcare requires two critical components: patients' best interests and best decisions to achieve that goal. The first goal is the lodestar, unchanged and unchanging over time. The second component is a more dynamic and rapidly changing paradigm in healthcare. Clinical decision-making has transitioned from an opinion-based paradigm to an evidence-based and data-driven process. A realization that technology and artificial intelligence can bring value adds a third component to the decision process. And the fertility sector is not exempt. The debate about AI is front and centre in reproductive technologies. Launching the transition from a conventional provider-driven decision paradigm to a software-enhanced system requires a roadmap to enable effective and safe implementation. A key nodal point in the ascending arc of AI in the fertility sector is how and when to bring these innovations into the ART routine to improve workflow, outcomes, and bottom-line performance. The evolution of AI in other segments of clinical care would suggest that caution is needed as widespread adoption is urged from several fronts. But the lure and magnitude for the change that these tech tools hold for fertility care remain deeply engaging. Exploring factors that could enhance thoughtful implementation and progress towards a tipping point (or perhaps not) should be at the forefront of any 'next steps' strategy. The objective of this Opinion is to discuss four critical areas (among many) considered essential to successful uptake of any new technology. These four areas include value proposition, innovative disruption, clinical agency, and responsible computing.